Continuous Change Detection and Classification Using Hidden Markov Model: A Case Study for Monitoring Urban Encroachment onto Farmland in Beijing

Remote Sensing

Yuan Yuan³  Yu Meng³  Lei Lin³  Hichem Sahli²  Anzhi Yue³  Jingbo Chen³  Zhongming Zhao³  Yunlong Kong³  Dongxu He³  Giles M. Foody¹  Parth Sarathi Roy¹ 

[1] Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;;Electronics and Informatics Department, Vrije Universiteit Brussel, Pleinlaan 2, BE-1050 Brussels, Belgium; E-Mail:;Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; E-Mails:

关键词: classification;    change detection;    hidden semi-Markov model (HSMM);    satellite image time series;    urban encroachment onto farmland;   

DOI  :  10.3390/rs71115318

来源: mdpi

【 摘 要 】

In this paper, we propose a novel method to continuously monitor land cover change using satellite image time series, which can extract comprehensive change information including change time, location, and “from-to” information. This method is based on a hidden Markov model (HMM) trained for each land cover class. Assuming a pixel’s initial class has been obtained, likelihoods of the corresponding model are calculated on incoming time series extracted with a temporal sliding window. By observing the likelihood change over the windows, land cover change can be precisely detected from the dramatic drop of likelihood. The established HMMs are then used for identifying the land cover class after the change. As a case study, the proposed method is applied to monitoring urban encroachment onto farmland in Beijing using 10-year MODIS time series from 2001 to 2010. The performance is evaluated on a validation set for different model structures and thresholds. Compared with other change detection methods, the proposed method shows superior change detection accuracy. In addition, it is also more computationally efficient.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.